Learning Outcomes

• Build valuable applied data storage, integration, and migration skills employers need.
• Gain hands-on experience using industry-specific data tools.
• Demonstrate you understand data-related best practices and can apply methodologies through industry-standard processes.
• Showcase your ability to solve problems related to data processes that you can talk about in interviews.

Learning Content

• Data Integration
  • Overview of data integration and implementation patterns
  • Data connectors and their necessity for effective integration
  • Data integration operations management (DIOM), security, architecture, and tools
• Data Storage
  • Overview of data storage and key concepts
  • File, block, object, and hybrid storage architectures
  • Cloud storage and its ubiquitous use across industries
  • Significance of backups and common backup techniques
• Data Migration and Final Project
  • Data migration concepts and related terminology
  • Migration architecture and processes
  • Physical location and cloud-to-cloud migration
  • Scenario-based project as a junior data migration specialist

Learning Outcomes

• Describe the core principles and techniques of predictive analytics
• Create predictive models to forecast outcomes
• List the key elements of building predictive models
• Evaluate and refine predictive models for enhanced accuracy, interpretability, and reliability
• Apply predictive analytics techniques to optimize operations and improve organizational efficiency
• Analyse real-world case studies to understand the practical application of predictive analytics in diverse industries
• Recall the impact of predictive analytics on key business functions, such as supply chain and customer retention
• Outline strategies for leveraging predictive analytics to transform and innovate business processes
• Review the diverse applications of predictive analytics across different business functions
• Discuss the role of predictive analytics in modern business decision-making
• Identify effective methods for gathering and preparing data for predictive analytics
• Explain the ability of predictive models to support decision-making in various domains

Learning Content

• Module 1: Understanding Predictive Analytics
  • Core concepts, methods, and practical applications of predictive analytics
  • Develop predictive models and identify critical trends
• Module 2: Course Assessment

Learning Outcomes

• Foundational programming skills with basic Python Syntax.
• How to use objects, classes and methods.
• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate from Meta

Learning Content

• Getting started with Python
  • module1
• Basic programming with Python
  • Module2
• Programming paradigms
  • Module3
• Modules, packages, libraries and tools
  • Module4
• End-of-course Graded Assessment
  • Module5

Learning Outcomes

• Basics of analytics terminology
• How data is used to make business decisions
• Identify the ideal analytical methodology for your specific needs
• Understand ways to collect, analyze, and visualize data
• Descriptive Analytics and how they are embedded in most organizations
• An understanding of how predictive models can improve your ability to make decisions in an uncertain world
• Prescriptive Analytics and how it helps to formulate recommendations of what you should do
• What is Data Management: Architecture, Quality and Privacy
• Master fundamental concepts and practices of the analytics life cycle and understand the best practices for each stage

Learning Content

• Welcome
• What is Analytics?
• Analytics Landscape
• Descriptive Analytics
• Predictive Analytics
• Prescriptive analytics
• Data Management
• Data-Analtyics Life cycle
• Course Wrap-up

Learning Outcomes

• Digital Twin concepts and evolution
• Practical industry applications
• Strategies for optimization and innovation
• Industry 4.0 and Digital Manufacturing
• Professionals in Manufacturing
• Engineering Students
• Anyone Interested in Digital Twin technology
• Anyone Interested in Industry 4.0/Digital Manufacturing

Learning Content

• Industry 4.0 and Introduction
• Basics of Digital Twins
• Digital Twin Architecture
• Modeling and Simulation
• Data Analytics and AI in Digital Twins
• Implementation of Digital Twins
• Digital Twin in Product Lifecycle Management (PLM)
• Digital Twin in Smart Manufacturing
• Maintenance and Optimization

Learning Outcomes

• Apply your skills to recognize real-world generative AI uses and identify popular generative AI models and tools.
• Gain knowledge of generative AI prompt engineering concepts, examples, common tools and techniques needed to create effective, impactful prompts.
• Identify appropriate generative AI tools for data analytics.
• Examine real-world applications where generative AI can enhance data analytics workflows.
• Generate text, images, and code using Generative AI
• Apply prompt engineering techniques and best practices
• Use Generative AI models to draw data insights from a survey conducted by a healthcare consultancy firm

Learning Content

• Generative AI: Introduction and Applications
• Generative AI: Prompt Engineering Basics
• Generative AI: Enhance your Data Analytics Career

Learning Outcomes

• Integrate Generative AI into existing data flows and data engineering lifecycle
• Generate and augment data using Generative AI
• Write data engineering code with Generative AI
• Explore Generative AI tools for data engineering
• Parse and extract insights and data from unstructured text using Generative AI
• Query and analyze data in data engineering with Generative AI
• Enrich, normalize, and standardize data using Generative AI features

Learning Content

• Environment Setup
• Data Generation and Augmentation
• Writing Data Engineering Code with Generative AI
• Gen AI Data Engineering Tools
• Data Parsing and Extraction
• Data Querying and Analysis
• Data Enrichment, Normalization, and Standardization

Learning Outcomes

• Understand the basics of digital twins, digital twins platform and ecosystem
• Learn the implementation of digital twins in manufacturing, the corresponding business values, and risks
• Get to know the future trends of digital twins and digital threads
• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool

Learning Content

• What is Digital Twins?
  • Learn the basics behind this technology
  • Describe the applications and uses for digital twins within a manufacturing setting
• Digital Twins Platform, Ecosystem, and Business Context
  • Address the digital twin platform ecosystem
  • Understand the business context and advantages of digital twins
  • Review risks and challenges surrounding this technology
• Future Trends and Summary
  • Learn about the forecast of future trends for digital twins
  • Explore the related concept of digital threads
  • Work through a case project for your final assessment